AB0047 CARTILAGE DEGRADATION STATUS OF KNEE OSTEOARTHIRTIC PATIENTS CAN BE PREDICTED BY CHONDROCYTE GENE EXPRESSION ANALYSIS
Authors: L. Tío, L. Triginer, A. Ribes, P. Camps, R. Torres-Claramunt, J. C. Monllau, J. Monfort
Journal: Annals of the Rheumatic Diseases (2022)

Background Osteoarthritis (OA) is the most common degenerative joint disease, but its pathogenesis is still poorly understood. OA is characterized by articular cartilage loss due to an imbalance in anabolic and catabolic gene expression of the chondrocyte, together with aberrant expression of transcription factors and inflammatory mediators. Objectives We aim to evaluate de genes implicated in the chondrocyte failure regarding the four issues assessed to evaluate cartilage integrity using Mankin score (MkS): surface integrity, cellularity, cell cloning and staining. Methods Knee OA (KOA) patients aged 60-75 undergoing a total knee replacement, were recruited. At surgery, tibial plateau and femur condyles were deposited in DMEM medium and sent to the laboratory within 4 hours of the extraction. Punches from areas located at low, medium and high load (1) were excised from each sample (approximately 11 areas per patient). For RNA analysis, punches were kept in RNAlater at -20ºC until analyzed. RNA extraction, cDNA synthesis and cDNA amplification of the selected genes were performed previous to gene expression quantifycation by OpenArray Assay with custom plates. ΔCt was analyzed by ExpressionSuite V1.3 using 6 housekeeping genes. For histology study, punchs were fixated in formalin and embedded in parafin. Sections of 4 µm were stained with hematoxilin eosin and Safranin O-Fast Green. Slices were scored using the modified MkS (2) by 3 independent observers. R (v3.3.2) analysis was used to explored the results with principal components analysis (PCA) and linear regression (LR). Results Surgical samples were obtained from a total of 27 patients and 219 different areas were analyzed. ΔCt of the 51 genes analyzed by PCA showed that 9 components were enough to represent &gt;80% of variance of the model. The genes associated with each component are summarized in Table 1. LR shows that second and first component were significantly associated to all or most of the fields included in the MkS, respectively. In contrast, component 4, 5 and 9 were not associated with any field. Lower expression of genes represented in component 1 (mainly related with extracellular matrix content) are associated with higher MkS values. On the other hand, component 2 summarizes data of genes mainly related with factors regulating cell metabolism. In this case, higher expression of TNFαIP6, NGF, IGFBP1, CRLF1, TNFα, and lower expression of ACAN, COL11A2, COL2A1, COL10A1, SOX9 and TGFβ1, among others, are associated with increased MkS values. Table 1. Genes that most contribute (by order) to component 1 &amp; 2 (due to space limitation). Data of LR analysis for each component and MkS item. Significant association marked in bold. Component Genes Mankin score item (β coefficient; p-value) Surface integrity Cellularity Cell cloning Staining 1 SERPINE2, COL8A2, OGN, COL5A, TIMP3, BMP6, CRLF1, TIMP2, WISP1, ST3GAL1, SERPINE1, TNFSF10, ASPN, NGF, CD44, TNFRSF11B, TGFA, ADAMTS5, COL15A1, LUM, TNFAIP6, PTGS2, BMP2 -0.092; 0.006 -0.025; 0.047 -0.028; 0.078 -0.043; 0.015 2 ACAN, GPC5, CFH, FGFR3, COL11A2, TGFB1, GREM1, TNFAIP6, NGF, SOX9, IGFBP1, MMP3, FGF2, CRLF1, TNFa, COL2A1, COL10A1 0.261; 2.54 10 -7 0.050; 0.010 0.066; 0.005 0.072; 0.007 3 -0.174; 0.003 -0.029; 0.191 -0.076; 0.006 0.004; 0.898 4 0.068; 0.410 -0.019; 0.543 -0.055; 0.161 -0.021; 0.627 5 -0.082; 0.364 0.051; 0.146 0.020; 0.644 -0.052; 0.283 6 0.050; 0.605 0.023; 0.548 0.111; 0.017 0.062; 0.228 7 -0.120; 0.266 -0.090; 0.032 0.010; 0.839 -0.113; 0.052 8 -0.134; 0.274 0.006; 0.892 -0.198; 0.001 -0.131; 0.047 9 -0.220; 0.096 -0.041; 0.425 -0.101; 0.110 0.020; 0.780 Conclusion There is a correlation between the expression level of the studied genes and the MkS values, although the role played by each gene might be different according to the MkS issue evaluated. These results could be useful to describe biomarkers that identify burden of disease in KOA. References [1]Adouni M, Shirazi-Adl A. J Orthop Res. 2014 Jan;32(1):69-78. [2]Moody HR, Heard BJ, et al. J Anat. 2012 Jul;221(1):47-54. Acknowledgements We acknowledge Berend Bremmen for his help in Mankin Score bibliography revision and image scoring. Disclosure of Interests None declared